FFLtool: a web server for transcription factor and miRNA feed forward loop analysis in human

2019 ◽  
Vol 36 (8) ◽  
pp. 2605-2607 ◽  
Author(s):  
Gui-Yan Xie ◽  
Mengxuan Xia ◽  
Ya-Ru Miao ◽  
Mei Luo ◽  
Qiong Zhang ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Web Server ◽  
Supplementary Information ◽  
Mechanism Study ◽  
Loop Analysis ◽  
Feed Forward ◽  
Feed Forward Loop

Abstract Summary Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The ‘FFL Analysis’ module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The ‘Browse FFLs’ module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases. Availability and implementation FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RTNduals: an R/Bioconductor package for analysis of co-regulation and inference of dual regulons

2019 ◽  
Vol 35 (24) ◽  
pp. 5357-5358
Author(s):  
Vinicius S Chagas ◽  
Clarice S Groeneveld ◽  
Kelin G Oliveira ◽  
Sheyla Trefflich ◽  
Rodrigo C de Almeida ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Supplementary Information ◽  
Transcriptional Networks ◽  
Bioconductor Package ◽  
Multiple Target ◽  
R Language ◽  
Downstream Effects ◽  
Bioconductor Project ◽  
General Method

Abstract Motivation Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons. Results RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts. Availability and implementation RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals T-Gene: improved target gene prediction

2020 ◽  
Vol 36 (12) ◽  
pp. 3902-3904
Author(s):  
Timothy O’Connor ◽  
Charles E Grant ◽  
Mikael Bodén ◽  
Timothy L Bailey
Keyword(s):  
Binding Sites ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Regulatory Element ◽  
Statistical Significance ◽  
Web Server ◽  
Supplementary Information ◽  
Expression Data ◽  
Command Line Tool

Abstract Motivation Identifying the genes regulated by a given transcription factor (TF) (its ‘target genes’) is a key step in developing a comprehensive understanding of gene regulation. Previously, we developed a method (CisMapper) for predicting the target genes of a TF based solely on the correlation between a histone modification at the TF’s binding site and the expression of the gene across a set of tissues or cell lines. That approach is limited to organisms for which extensive histone and expression data are available, and does not explicitly incorporate the genomic distance between the TF and the gene. Results We present the T-Gene algorithm, which overcomes these limitations. It can be used to predict which genes are most likely to be regulated by a TF, and which of the TF’s binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a gene’s promoter, achieving median precision above 60%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median precision above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions. Availability and implementation The T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://meme-suite.org. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MicroRNAs reinforce repression of PRC2 transcriptional targets independently and through a feed-forward regulatory network

2018 ◽  
Author(s):  
Haridha Shivram ◽  
Steven V. Le ◽  
Vishwanath R. Iyer
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Target Genes ◽  
Significant Proportion ◽  
Feed Forward ◽  
Transcriptional Targets ◽  
Polycomb Repressive Complex 2 ◽  
Small Regulatory Rnas

AbstractGene expression can be regulated at multiple levels, but it is not known if and how there is broad coordination between regulation at the transcriptional and post-transcriptional levels. Transcription factors and chromatin regulate gene expression transcriptionally, while microRNAs (miRNAs) are small regulatory RNAs that function post-transcriptionally. Systematically identifying the post-transcriptional targets of miRNAs and the mechanism of transcriptional regulation of the same targets can shed light on regulatory networks connecting transcriptional and post-transcriptional control. We used iCLIP (individual crosslinking and immunoprecipitation) for the RISC (RNA-induced silencing complex) component AGO2 and global miRNA depletion to identify genes directly targeted by miRNAs. We found that PRC2 (Polycomb repressive complex 2) and its associated histone mark, H3K27me3, is enriched at hundreds of miRNA-repressed genes. We show that these genes are directly repressed by PRC2 and constitute a significant proportion of direct PRC2 targets. For just over half of the genes co-repressed by PRC2 and miRNAs, PRC2 promotes their miRNA-mediated repression by increasing expression of the miRNAs that are likely to target them. miRNAs also repress the remainder of the PRC2 target genes, but independently of PRC2. Thus, miRNAs post-transcriptionally reinforce silencing of PRC2-repressed genes that are inefficiently repressed at the level of chromatin, by either forming a feed-forward regulatory network with PRC2 or repressing them independently of PRC2.

scholarly journals miRDRN—miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7309
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Gene Annotation ◽  
Biological Pathways ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Disease Pair

Background MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action. Methods Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5951 ◽  
Author(s):  
Ruijiang Li ◽  
Hebing Chen ◽  
Shuai Jiang ◽  
Wanying Li ◽  
Hao Li ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Mirna Gene ◽  
Web Tool ◽  
Cancer Types ◽  
Regulatory Analysis ◽  
Tf Gene ◽  
User Friendly ◽  
Functional Analyses

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.

scholarly journals Identification of the TF-miRNA-mRNA Co-regulatory Networks Involved in Sepsis

2021 ◽  
Author(s):  
Xiaoqian Luo ◽  
Weina Lu ◽  
Jianfeng Zhao ◽  
Jun Hu ◽  
Enjiang Chen ◽  
...  
Keyword(s):  
Expression Analysis ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Medical Condition ◽  
Differentially Expressed ◽  
Biological Targets ◽  
Differentially Expressed Mirnas

Abstract BackgroundSepsis is a life-threatening medical condition caused by a dysregulated host response to infection. Recent studies have found that the expression of miRNAs is associated with the pathogenesis of sepsis and septic shock. Our study aimed to reveal which miRNAs may be involved in the dysregulated immune response in sepsis and how these miRNAs interact with transcription factors (TFs) using a computational approach with in vitro validation studies. MethodsTo determine the network of TFs, miRNAs and target genes involved in sepsis, GEO datasets GSE94717 and GSE131761 were used to identify differentially expressed miRNAs and DEGs. TargetScan and miRWalk databases were used to predict biological targets that overlap with the identified DEGs of differentially expressed miRNAs. The TransmiR database was used to predict the differential miRNA TFs that overlap with the identified DEGs. The TF-miRNA-mRNA network was constructed and visualized. Finally, qRT-PCR was used to verify the expression of TFs and miRNA in HUVECs. ResultBetween the healthy and sepsis groups, there were 146 upregulated and 98 downregulated DEGs in the GSE131761 dataset, and there were 1 upregulated and 183 downregulated DEMs in the GSE94717 dataset. A regulatory network of the TF-miRNA-target genes was established. According to the experimental results, RUNX3 was found to be downregulated while MAPK14 was upregulated, which corroborates the result of the computational expression analysis. In a HUVECs model, miR-19b-1-5p and miR-5009-5p were found to be significantly downregulated. Other TFs and miRNAs did not correlate with our bioinformatics expression analysis. ConclusionWe constructed a TF-miRNA-target gene regulatory network and identified potential treatment targets RUNX3, MAPK14, miR-19b-1-5p and miR-5009-5p. This information provides an initial basis for understanding the complex sepsis regulatory mechanisms.

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